The proposed research aims to: a) Improve the understanding of the genetics of inherited diseases with unclear modes of transmission. Studies will evaluate the effectiveness of current methods of analysis, including classical linkage analysis, sib-pair or affected-pedigree- member analysis and the use of measures of association in understanding the underlying genetic mechanisms of such traits. Simulation studies will continue to provide a source of family data reflecting confounding factors thought to be a problem in linkage analysis of certain complex traits, such as psychiatric or behavioral disorders. Factors to be considered include assortative mating, genetic heterogeneity and multi- locus disease determination. The ability of current methods to correctly analyze traits with one or more of these factors will be assessed and, where appropriate, alternative methods will be developed and tested. b) Apply techniques of neural network pattern matching to problems of genetic systems. Applications include: aid in phenotype definition for traits with multiple clinical problems of genetic systems. Applications include: aid in phenotype definition for traits with multiple clinical characteristics; determination of risk of disease based on phenotype, known risk factors and disease profiles in relatives; determination of organ transplant success based on HLA antigen matching patterns; definition of disease phenotype based on quantitative factors. c) Develop and apply strategies for ordering multiple linked loci using pairwise recombination data, radiation hybrid data, or other physical mapping data. Some of these ordering strategies may be adaptable to the development of techniques for integrating map information obtained by different methods, an important step in organizing a comprehensive, reliable map. d) Carry out classical linkage analysis for specific genetic diseases. Currently, a genome scan is underway to identify a gene or genes for polycystic liver disease. Other diseases to be studied include lymphoma and prostate cancer. Methods to be tested in the simulation studies can be applied to these analyses in order to better understand the complete genetic picture, including identification of heterogeneity, by detecting linkage of different disease forms to different marker loci. Such differentiation will help sharpen the clinical definition of various forms of the diseases. As a result of advances from this work, better mathematical tools for the study of diseases with complex or ill-defined inheritance patterns will be available. Applications to specific diseases will increase understanding of interactions between clinical definition and predisposing genetic factors. This will increase the precision of genetic counseling and suggest useful approaches for studying the mechanisms involved in determining disease state.